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pro vyhledávání: '"Narnhofer, Dominik"'
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that ar
Externí odkaz:
http://arxiv.org/abs/2405.18087
Autor:
Halbheer, Michelle, Mühlematter, Dominik J., Becker, Alexander, Narnhofer, Dominik, Aasen, Helge, Schindler, Konrad, Turkoglu, Mehmet Ozgur
Numerous crucial tasks in real-world decision-making rely on machine learning algorithms with calibrated uncertainty estimates. However, modern methods often yield overconfident and uncalibrated predictions. Various approaches involve training an ens
Externí odkaz:
http://arxiv.org/abs/2405.14438
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network. This lets us estimate the likelih
Externí odkaz:
http://arxiv.org/abs/2403.14497
Autor:
Benfenati, Alessandro, Chouzenoux, Emilie, Franchini, Giorgia, Latva-Aijo, Salla, Narnhofer, Dominik, Pesquet, Jean-Christophe, Scott, Sebastian J., Yousefi, Mahsa
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other supervised methods and remain one of the most popular approaches in th
Externí odkaz:
http://arxiv.org/abs/2308.16858
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that ar
Externí odkaz:
http://arxiv.org/abs/2303.05966
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in o
Externí odkaz:
http://arxiv.org/abs/2212.12499
Autor:
Narnhofer, Dominik, Effland, Alexander, Kobler, Erich, Hammernik, Kerstin, Knoll, Florian, Pock, Thomas
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However, the model-immanent (epistemic) uncertainty is less frequently systemat
Externí odkaz:
http://arxiv.org/abs/2102.06665
Publikováno v:
J. Magn. Reson. 286 (2018) 148-157
In the search for a novel MRI contrast agent which relies on T1 shortening due to quadrupolar interaction between Bi nuclei and protons, a fast scanning wideband system for zero-field nuclear quadrupole resonance (NQR) spectroscopy is required. Estab
Externí odkaz:
http://arxiv.org/abs/1801.01299
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